Lateral spatial biases in naturalistic and simulated driving: Does pseudoneglect influence performance?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whereas a rightward bump is more likely than a leftward bump when walking through a doorway, investigations into potential similar asymmetries for drivers are limited. The research presented here aims to determine the influence of innate lateral spatial biases when driving. Data from the Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and a driving simulation were used to address our research questions. Data points from SHRP 2 were aggregated within relevant variables (e.g., left/right obstacles). In the simulation, participants drove in ways that were consistent with their everyday driving in urban and rural environments. Collision frequency, collision severity and average lateral lane position were analyzed with rightward biases throughout both analyzes. SHRP 2 data indicated greater likelihoods of collisions when vehicles crossed the right line/edge of the road and when making a right turn. There were more collisions with obstacles on the right side, which were also more severe, and greater rightward lane deviations in the driving simulation, contrasted with more severe collisions on the left side in SHRP 2 data, possibly because of the presence of traffic. These findings suggest that previously observed rightward biases in distant space when walking are also present when driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it